AI SearchData mining techniques for predicting the survival of passangers on the Titanic (Record no. 25627)
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| fixed length control field | 200710s2016 th uu m rtt 0| a1eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12345283 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT RSPR no.IM-16-05 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bakiev, Sabit Kenjebaevich |
| 245 10 - TITLE STATEMENT | |
| Title | Data mining techniques for predicting the survival of passangers on the Titanic |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2016 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 30 p. : |
| Other physical details | ill. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Research studies project report ; |
| Volume/sequential designation | no. IM-16-05 |
| 500 ## - GENERAL NOTE | |
| General note | A researchsubmitted in partial fulfillment of the requirements for thed egree of Masterof Science in Information Management, School of Engineering and Technology |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Research studies project report (M. Sc.) - Asian Institute of Technology, 2016 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Mining techniques have proven to be effective in exploring data. In this report, the efficiency of several data-mining methods is explored. In particular, we apply these methods to the predictive modelling competition Titanic: Machine Learning from Disaster currently active at kaggle.com, a website for such competitions. This particular competition is a classification challenge to build a model to predict which passengers on the Titanic survived. The focus of our approach is comparing different data-mining techniques such as K-neighbourhood, Logistic Regression, Support Vector Machine, XGBoost, Linear Regression, Stochastic Gradient Decent, Decision Tree, Naive Bayes and Random Forest algorithms. Results indicate that the predictors' gender, ticket price, embarked port, age, title, and passenger class are the most important variables to predict survival of the passengers. According to the results, Random Forest classifier has gained the highest accuracy of nine classifiers with a score: "0.80861" (322 out of 3667) top 10% on the Titanic: Machine Learning from Disaster Competition. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data mining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Guha, Sumanta, |
| Relator term | Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vatcharaporn Esichaikul, |
| Relator term | Examination Committee |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huynh, Trung Luong, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Development Bank - Japan Scholarship Program (ADB-JSP), |
| Relator term | Scholarship donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Research studies project report ; |
| Volume/sequential designation | no. IM-16-05 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B05037">http://203.159.5.9/ait-thesis/detail.php?q=B05037</a> |
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| Cataloger's initials, CIN (RLIN) | 200710 |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type | Cost, normal purchase price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT RSPR no.IM-16-05 | 30050120891287 | 18/08/2026 | 1 | 18/08/2026 | 40-Archives | |||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT RSPR no.IM-16-05 | 18/08/2026 | 18/08/2026 | 61-CD-ROM | |||||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT RSPR no.IM-16-05 | 30050120982888 | 18/08/2026 | 1 | 18/08/2026 | 20-AIT Publication | 50.00 | ||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT RSPR no.IM-16-05 | 30050120982870 | 18/08/2026 | 2 | 18/08/2026 | 20-AIT Publication | 50.00 |